SAS Enterprise Miner: Data Mining and Predictive Modeling

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1. SAS Predictive Modeling 01 - Introduction

1. Introduction of SAS Enterprise Miner.mp4

11:35

2. Select a SAS Table.mp4

09:54

3. Creating Input Data Node.mp4

12:58

4. Metadata Advisor Options.mp4

08:53

5. Add More Data Sources.mp4

11:01

6. Sample Statistics.mp4

10:22

7. Trial report.mp4

09:32

8. Properties of Cluster Node.mp4

08:06

9. Variable Selection.mp4

08:55

2. SAS Predictive Modeling 02 - Variables

1. Input Variable.mp4

09:38

2. Values of R-Square.mp4

09:22

3. Binary Target Variable.mp4

08:41

4. Variable and Effect Summary.mp4

09:16

5. Variable Selection - Variable IDs.mp4

08:39

6. Variable Frequency Table.mp4

09:12

7. Variable S - Updating Model Comparison.mp4

08:47

8. Run Data Partition Node.mp4

08:14

9. Variable Selection - Fit Statistics.mp4

09:22

10. Understanding Transformation of Variables.mp4

09:37

11. Score Ranking Overlay Res.mp4

09:08

12. Update Transformation of Variables.mp4

09:46

3. SAS Predictive Modeling 04 - Neural Networks

1. Neural Network Model.mp4

10:10

2. Neural Network Model Output.mp4

09:40

3. Model Weight History.mp4

12:28

4. Neural Network - Final Weight.mp4

06:08

5. ROC Chart.mp4

07:41

6. Neural Network -Iteration Plot.mp4

08:45

7. Neural Network - SAS Code.mp4

10:09

8. Neural Network - Cumulative Lift.mp4

06:23

9. Decision Processing.mp4

06:22

10. Results of Auto Neural Node.mp4

07:01

11. Run Model Comparison.mp4

08:00

12. DEX - Variable IDs.mp4

10:48

13. Average Square Error.mp4

06:14

14. Score Rating overlay - Event.mp4

05:41

15. Run Domine Regression Node.mp4

05:53

4. SAS Predictive Modeling 05 - Regression

1. Regression with Binary Target.mp4

07:59

2. Regression - Table Effect Plots.mp4

07:43

3. Result of Regression Model.mp4

08:53

4. Update Regression Node.mp4

08:56

5. Creating Flow Diagram.mp4

08:40

5. Logistic Regression Project using SAS Stat

1. Introduction to Logistic Regression Project using SAS Stat.mp4

11:30

2. Insurance Dataset Explanation and Exploration.mp4

15:31

3. Logistic Regression Demonstration Part 1.mp4

14:16

4. Logistic Regression Demonstration Part 2.mp4

27:00

5. Missing Values Imputation.mp4

22:37

6. Categorical Inputs.mp4

11:29

7. Categorical Inputs Continue.mp4

13:02

8. Variable Clustering Part 1.mp4

11:30

9. Variable Clustering Part 2.mp4

06:50

10. Variable Clustering Part 3.mp4

07:46

11. Variable Screening.mp4

11:09

12. Variable Screening Continue.mp4

08:51

13. Logit Plots.mp4

15:17

14. Subset Selection Part 1.mp4

11:59

15. Subset Selection Part 2.mp4

11:12

16. Subset Selection Part 3.mp4

10:02

17. Subset Selection Part 4.mp4

09:22

18. Subset Selection Part 5.mp4

10:33

19. Subset Selection Part 6.mp4

10:34

20. Subset Selection Part 7.mp4

09:12

21. Subset Selection Part 8.mp4

09:44

Description

Master predictive modeling and data mining using SAS Enterprise Miner.

What You'll Learn?

Introduction to SAS Enterprise Miner and its capabilities for predictive modeling and data mining.
Importing datasets in various formats such as text, CSV, xlsx, and xls.
Understanding user operating concepts and software menus within SAS Enterprise Miner.
Exploring statistical concepts like mean, standard deviation, and sample statistics.
Performing variable selection using techniques like input variables, R-square values, and binary target variables.
Combining different modeling techniques such as decision trees, neural networks, and regression models for enhanced predictive accuracy.
Building and evaluating neural network models, including model weight history, ROC charts, and iteration plots.
Implementing regression analysis with binary targets, interpreting regression model results, and creating effect plots.
Engaging in practical exercises, case studies, and interactive discussions to reinforce learning.

Who is this for?

Data analysts and scientists seeking to deepen their understanding of advanced analytics tools.

Business intelligence professionals aiming to leverage predictive modeling for decision-making.

Students and academics interested in learning practical applications of statistical modeling.

Professionals in industries such as finance, healthcare, marketing, and retail looking to apply predictive analytics to their domain-specific datasets.

Anyone keen on mastering techniques like variable selection, neural networks, regression analysis, and decision trees using SAS Enterprise Miner.

What You Need to Know?

Prior knowledge of Quantitative Methods, MS Office and Data will be useful

More details

Description
Welcome to our course on SAS Enterprise Miner! In this comprehensive program, you will delve into the intricacies of predictive modeling and data mining using one of the industry's leading tools, SAS Enterprise Miner. Throughout this course, you will learn how to leverage the powerful features of SAS Enterprise Miner to extract meaningful insights from your data, build robust predictive models, and make informed business decisions. Whether you're a seasoned data analyst or a beginner in the field, this course will equip you with the skills and knowledge needed to excel in the world of data science and analytics using SAS Enterprise Miner. Join us on this exciting journey as we explore the vast capabilities of SAS Enterprise Miner and unlock the potential of your data!
Section 1: SAS Enterprise Miner Intro
In this section, you'll receive a comprehensive introduction to SAS Enterprise Miner, a powerful tool for predictive modeling and data mining. Starting with the basics, you'll learn how to navigate the interface, select datasets, and create input data nodes. Through hands-on demonstrations, you'll explore various features such as metadata advisor options, sample statistics, and trial reports, laying a strong foundation for your journey ahead.
Section 2: SAS Enterprise Miner Variable Selection
This section focuses on variable selection techniques in SAS Enterprise Miner. You'll delve into concepts like input variables, R-square values, and binary target variables. Through practical exercises, you'll gain insights into variable selection methods, frequency tables, and model comparison. By the end of this section, you'll be equipped with the skills to effectively choose and analyze variables for your predictive models.
Section 3: SAS Enterprise Miner Combination
In this section, you'll learn how to combine different models in SAS Enterprise Miner to enhance predictive accuracy. You'll explore techniques like decision trees, neural networks, and regression models. Through interactive sessions, you'll understand model iteration plots, subseries plots, and ensemble diagrams. By the end of this section, you'll be proficient in combining and analyzing diverse modeling techniques for optimal results.
Section 4: SAS Enterprise Miner Neural Network
This section delves into neural network modeling using SAS Enterprise Miner. You'll learn about neural network architectures, model weight history, and ROC charts. Through practical examples, you'll gain hands-on experience in building and evaluating neural network models. By mastering neural network techniques, you'll be able to tackle complex data mining tasks and extract valuable insights from your data.
Section 5: SAS Enterprise Miner Regression
In this final section, you'll explore regression modeling techniques in SAS Enterprise Miner. You'll learn how to perform regression analysis with binary targets, interpret regression model results, and create effect plots. Through step-by-step tutorials, you'll understand the intricacies of regression modeling and its applications in predictive analytics. By the end of this section, you'll have a solid understanding of regression techniques and their role in data-driven decision-making.
Throughout the course, you'll engage in practical exercises, real-world case studies, and interactive discussions to reinforce your learning. Whether you're a novice or an experienced data scientist, this course will empower you to harness the full potential of SAS Enterprise Miner for predictive modeling and data analysis.
Who this course is for:
Data analysts and scientists seeking to deepen their understanding of advanced analytics tools.
Business intelligence professionals aiming to leverage predictive modeling for decision-making.
Students and academics interested in learning practical applications of statistical modeling.
Professionals in industries such as finance, healthcare, marketing, and retail looking to apply predictive analytics to their domain-specific datasets.
Anyone keen on mastering techniques like variable selection, neural networks, regression analysis, and decision trees using SAS Enterprise Miner.

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